abstract: Imagine that we have a piece of matter that can change its physical properties like its shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Envisioning systems of nano-sensors devices, programmable matter consists of systems of simple computational elements, called particles, that can establish and release bonds, compute, and can actively move in a self-organized way. In this dissertation the feasibility of solving fundamental problems relevant for programmable matter is investigated. As a model for such self-organizing particle systems (SOPS), the geometric amoebot model is introduced. In this model, particles only have local information and have modest computational power. They achieve locomotion by expanding and contracting, which resembles the behavior of amoeba. Under this model, efficient local-control algorithms for the leader election problem in SOPS are presented. As a central problem for programmable matter, shape formation problems are then studied. The limitations of solving the leader election problem and the shape formation problem on a more general version of the amoebot model are also discussed. The \smart paint" problem is also studied which aims at having the particles self-organize in order to uniformly coat the surface of an object of arbitrary shape and size, forming multiple coating layers if necessary. A Universal Coating algorithm is presented and shown to be asymptotically worst-case optimal both in terms of time with high probability and work. In particular, the algorithm always terminates within a linear number of rounds with high probability. A linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information is presented, which implies that the proposed algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of the proposed algorithm may be better than linear in practice. Developed algorithms utilize only local control, require only constant-size memory particles, and are asymptotically optimal in terms of the total number of particle movements needed to reach the desired shape configuration. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

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Este estudo propõe um modelo híbrido que reúne uma rede neural do tipo SOM (Self-Organizing Map) com uma rede neural do tipo Multicamadas com Retropropagação (BPN: Backpropagation Network). A utilização da rede SOM tem o intuito de segmentar a base de dados em diversos clusters, onde são ressaltadas suas diferenças. A rede BPN é usada para construir um modelo matemático de previsão que descreve a relação entre os indicadores e o valor de fechamento de cada cluster formado na rede SOM. A viabilidade e o percentual de efetividade do modelo proposto são demonstrados através de experimentos de predição de índices utilizados pelo NYSE (New York Stock Exchange). O modelo foi elaborado a partir de uma base de dados composta pelo índice NYSE Composite U.S. 100 no período entre 02 de abril de 2004 a 08 de novembro de 2012. Como variáveis de entrada para as redes neurais, foram utilizados 10 índices: MA_10, BIAS_20, WMS%R_9, K_9, D_9, MTM_10, ROC_10, CCI_24, AR_26, BR_26. Os resultados obtidos com o modelo híbrido proposto se mostraram superiores aos obtidos com modelos convencionais estatísticos. / This study proposes a hybrid model that combines a neural network SOM (Self-Organizing Map) with a neural network with Multilayer Backpropagation (BPN: Backpropagation Network). The SOM aims to segment the database into different clusters, where they highlight their differences. The BPN network is used to build a predictive mathematical model that describes the relationship between the indicators and the closing value of each cluster formed in the SOM. The percentage of viability and effectiveness of the proposed model are demonstrated through experiments predict index used by the NYSE (New York Stock Exchange). The model was developed from a database composed of 100 U.S. NYSE Composite Index in the period from April, 02, 2004 to November, 08, 2012. As input variables for neural networks, we used 10 indices: MA_10, BIAS_20, WMS%R_9, K_9, D_9, MTM_10, ROC_10, CCI_24, AR_26, BR_26. Results obtained with the proposed hybrid model were higher than those obtained with conventional statisticals techniques.

nÃo hà / In this dissertation it is reported the results of a comprehensive comparative study involving six local models applied to the task of learning the inverse kinematics of three redundant robotic arm (planar, PUMA 560 and Motoman HP6). The evaluated algorithms are the following ones: radial basis functions network (RBFN), local model network (LMN), SOMbased local linear mapping (LLM), local linear mapping over k-winners (K-SOM), local weighted regression (LWR) and counter propagation (CP). Each algorithm is evaluated with respect to its accuracy in estimating the joint angles given the cartesian coordinates which comprise end-effector trajectories within the robot workspace. A comprehensive evaluation of the performances of the aforementioned algorithms is carried out based on correlation analysis of the residuals. Finally, hypothesis testing procedures are also executed in order to verifying if there are significant differences in performance among the best algorithms. / Nesta dissertaÃÃo sÃo reportados os resultados de um amplo estudo comparativo envolvendo seis modelos locais aplicados à tarefa de aproximaÃÃo do modelo cinemÃtico inverso de 3 robÃs manipuladores (planar, PUMA 560 e Motoman HP6). Os modelos avaliados sÃo os seguintes: rede de funÃÃes de base radial (RBFN), rede de modelos locais (LMN), mapeamento linear local baseado em SOM (LLM), mapeamento linear local usando K vencedores (KSOM), regressÃo local ponderada (LWR) e rede counterpropagation (CP). Estes algoritmos sÃo avaliados quanto à acurÃcia na estimaÃÃo dos Ãngulos das juntas dos robÃs manipuladores em experimentos envolvendo a geraÃÃo de vÃrios tipos de trajetÃrias no espaÃo de trabalho dos referidos robÃs. Uma avaliaÃÃo abrangente do desempenho de cada algoritmo à feita com base na anÃlise dos resÃduos e testes de hipÃteses sÃo realizados para verificar a semelhanÃa estatistica entre os desempenhos dos melhores algoritmos.

Este estudo propõe um modelo híbrido que reúne uma rede neural do tipo SOM (Self-Organizing Map) com uma rede neural do tipo Multicamadas com Retropropagação (BPN: Backpropagation Network). A utilização da rede SOM tem o intuito de segmentar a base de dados em diversos clusters, onde são ressaltadas suas diferenças. A rede BPN é usada para construir um modelo matemático de previsão que descreve a relação entre os indicadores e o valor de fechamento de cada cluster formado na rede SOM. A viabilidade e o percentual de efetividade do modelo proposto são demonstrados através de experimentos de predição de índices utilizados pelo NYSE (New York Stock Exchange). O modelo foi elaborado a partir de uma base de dados composta pelo índice NYSE Composite U.S. 100 no período entre 02 de abril de 2004 a 08 de novembro de 2012. Como variáveis de entrada para as redes neurais, foram utilizados 10 índices: MA_10, BIAS_20, WMS%R_9, K_9, D_9, MTM_10, ROC_10, CCI_24, AR_26, BR_26. Os resultados obtidos com o modelo híbrido proposto se mostraram superiores aos obtidos com modelos convencionais estatísticos. / This study proposes a hybrid model that combines a neural network SOM (Self-Organizing Map) with a neural network with Multilayer Backpropagation (BPN: Backpropagation Network). The SOM aims to segment the database into different clusters, where they highlight their differences. The BPN network is used to build a predictive mathematical model that describes the relationship between the indicators and the closing value of each cluster formed in the SOM. The percentage of viability and effectiveness of the proposed model are demonstrated through experiments predict index used by the NYSE (New York Stock Exchange). The model was developed from a database composed of 100 U.S. NYSE Composite Index in the period from April, 02, 2004 to November, 08, 2012. As input variables for neural networks, we used 10 indices: MA_10, BIAS_20, WMS%R_9, K_9, D_9, MTM_10, ROC_10, CCI_24, AR_26, BR_26. Results obtained with the proposed hybrid model were higher than those obtained with conventional statisticals techniques.

O transporte pneumático de sólidos constitui uma aplicação comum em processos industriais petroquímicos, de mineração, de alimentos e agrícola. Entretanto, devido a limitações de ordem prática, a maioria das aplicações existente envolve o transporte de 1 a 400 toneladas por hora, através de distâncias de até 1000 m. Entre estas limitações, o consumo de potência provavelmente é a mais severa. Um sistema de transporte seguro e que apresente uma redução no consumo de potência pode ser implementado com técnicas não convencionais de controle. Este trabalho descreve a implementação de um controlador difuso em um circuito experimental de 45 mm de diâmetro interno utilizado para transportar sementes de Setaria Itálica ao longo de 21 metros. Informações obtidas com um estudo prévio de identificação de regimes gás - sólido através de redes neurais auto-organizáveis foram utilizadas no projeto do controlador. Os resultados mostraram uma redução significativa de 41%, em média, no consumo de potência requerida para o transporte de uma mesma carga de sólido. / The pneumatic conveying of solids in a gas stream is a recurrent process in petrochemical industries as well as in agricultural, food and mining. However, due to practical limitations the majority of existing systems have capacities ranging from 1 to 400 tones per hour over distances less than 1000 m, mainly because of a high power consumption per transported unit mass. A safe circuit with reduced power consumption can be designed using non-conventional control techniques. This work describes a fuzzy controller implementation for a 45 mm i.d. pneumatic conveying system used to transport Setaria Italica seeds over a distance of 21 m. Data obtained in a previous study about gas-solid flow regime identification through a self-organizing neural network were used in the controller design. The results show that reduction in power consumption can reach 41% when compared with classical non controller transport.

Orientador: Ítala Maria Loffredo D'Ottaviano / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Filosofia e Ciências Humanas / Made available in DSpace on 2018-08-19T12:55:05Z (GMT). No. of bitstreams: 1 Andrade_RamonSouzaCapelle_D.pdf: 2199404 bytes, checksum: 2cfe6779d4b1e626d3847b217ffd7a7f (MD5) Previous issue date: 2011 / Resumo: O objetivo desta Tese consiste em defender que (a) os condicionais constituem a forma lógica subjacente à manifestação das leis naturais, das leis biológicas e dos hábitos psicocomportamentais. Defendemos, também, que, (b) embora tenhamos a mesma forma lógica subjacente à manifestação dessas regularidades (leis, hábitos), não temos, contudo, o mesmo grau de conexão entre antecedentes e conseqüentes nesses contextos de realidade (físico-químico, biológico e psicocomportamental). Em concordância com a nossa interpretação de parte da Hipótese Cosmológica de Peirce, defendemos que: (c) as leis naturais fortemente determinam seus conseqüentes (Se o antecedente acontece, então o conseqüente quase-necessariamente se segue), (d) as leis biológicas moderadamente determinam seus conseqüentes (Se o antecedente acontece, então o conseqüente muito provavelmente se segue) e (e) os hábitos psicocomportamentais fracamente determinam seus conseqüentes (Se o antecedente acontece, então o conseqüente provavelmente se segue). Anexamos o rótulo Hipótese do Espectro de Determinação de Condicionais Causais a essas diferentes (quase-necessária, muito provável e provável) modalidades de conexão entre antecedentes e conseqüentes. Oferecemos (f), com base na semântica de David Lewis (2005), um modelo para esse espectro de determinação, e procuramos expressar a determinação do condicional e, ao mesmo tempo, deixar espaço para a indeterminação ou acaso. Procuramos (g) caracterizar sistema e organização, e (h) argumentamos que um hábito constitui um componente organizacional da estrutura psicocomportamental de um agente. Oferecemos (i) uma classificação dos hábitos em hábitos que estabelecem os traços da identidade do sistema/agente, hábitos racionais e hábitos degenerados. Procuramos ainda (j) caracterizar a auto-organização e (l) analisar como um processo de auto-organização secundária se estabelece na estrutura psicocomportamental de um agente / Abstract: The objective of this thesis consists in arguing that (a) conditionals constitute the logical form underlying the manifestation of natural laws, biological laws, and psycho-behavioral habits. It is also argued that (b) even though we find the same logical form underlying the manifestation of these regularities (laws and habits), we do not find, however, the same degree of connection between antecedents and consequents in the relevant contexts of reality (physico-chemical, biological, and psycho-behavioral). In accord with our interpretation of part of Peirce's cosmological hypothesis, we argue that (c) natural laws strongly determine their consequents (if the antecedent occurs, then the consequent almost necessarily follows), (d) biological laws moderately determine their consequents (if the antecedent occurs, then the consequent very probably follows), and (e) psycho-behavioral habits weakly determine their consequents (if the antecedent occurs, then the consequent probably follows) We use the appellation "hypothesis of the spectrum of determination of causal conditionals" to express these different modalities of connection between antecedents and consequents (almost-necessary, very probable, and probable). Based on the semantics of David Lewis (2005), we propose (f) a model for this spectrum of determination, and we seek to express the determination of the conditional and, at the same time, to allow room for indetermination or chance. We seek (g) to characterize system and organization, and we argue that (h) a habit constitutes an organizational component in the psycho-behavioral structure of an agent. We offer (i) a threefold classification of habits into habits that establish the features of the identity of the system/agent, rational habits, and degenerate habits. We seek (j) to characterize self-organization, and (k) to analyze how a process of secondary self-organization establishes itself in the psycho-behavioral structure of an agent / Doutorado / Filosofia / Doutor em Filosofia

Recent literature has investigated the use of different clustering techniques for analysis of gene expression data. For example, self-organizing maps (SOMs) have been used to identify gene clusters of clear biological relevance in human hematopoietic differentiation and the yeast cell cycle (Tamayo et al., 1999). Hierarchical clustering has also been proposed for identifying clusters of genes that share common roles in cellular processes (Eisen et al., 1998; Michaels et al., 1998; Wen et al., 1998). Systematic evaluation of clustering results is as important as generating the clusters. However, this is a difficult task, which is often overlooked in gene expression studies. Several gene expression studies claim success of the clustering algorithm without showing a validation of complete clusterings, for example Ben-Dor and Yakhini (1999) and Törönen et al. (1999). In this dissertation we propose an evaluation approach based on a relative entropy measure that uses additional knowledge about genes (gene annotations) besides the gene expression data. More specifically, we use gene annotations in the form of an enzyme classification hierarchy, to evaluate clusterings. This classification is based on the main chemical reactions that are catalysed by enzymes. Furthermore, we evaluate clusterings with pure statistical measures of cluster validity (compactness and isolation). The experiments include applying two types of clustering methods (SOMs and hierarchical clustering) on a data set for which good annotation is available, so that the results can be partly validated from the viewpoint of biological relevance. The evaluation of the clusters indicates that clusters obtained from hierarchical average linkage clustering have much higher relative entropy values and lower compactness and isolation compared to SOM clusters. Clusters with high relative entropy often contain enzymes that are involved in the same enzymatic activity. On the other hand, the compactness and isolation measures do not seem to be reliable for evaluation of clustering results.

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